Preprocessing the Iris Dataset for TensorFlow

Introduction

In today's lesson, our focus is on preprocessing the Iris dataset for TensorFlow. We will explore various techniques, such as data splitting, feature scaling, and one-hot encoding. This foundation is invaluable in the field of machine learning as it aids in understanding the intricacies of data transformation before we feed it to a neural network. Let's get into it!

Overview of the Iris Dataset

Before we delve into data preprocessing, it is imperative to understand the data we are processing. The Iris dataset comprises measurements from 150 Iris flowers coming from three different species. Each sample includes the following 4 features: Sepal length (cm) : e.g., 5.1, 4.9, 4.7, etc. Sepal width (cm) : e.g., 3.5, 3.0, 3.2, etc. Petal length (cm) : e.g., 1.4, 1.4, 1.3, etc. Petal width (cm) : e.g., 0.2, 0.2, 0.2, etc. Additionally, each sample has a class label representing the Iris species. The targets in the dataset are represented as one of the following options: Iris setosa : 0 Iris versicolor : 1 Iris virginica : 2 With these measurements and labels, the Iris dataset becomes a multivariate dataset often used for machine learning introductions.

Insight into Data Preprocessing

Data preprocessing is a crucial step in machine learning. It is the process of converting or mapping data from the initial form to another format to prepare the data for the next processing phase. This converted data could be easier for the algorithms to extract information, hence improving their ability to predict. The steps involved in preprocessing we will cover in today's lesson include data load, split, scale, and encode.

Step 1: Loading the Dataset

Before diving into preprocessing, let's start by loading the Iris dataset. We use the load_iris function from scikit-learn for this purpose. It returns the feature matrix X and the target vector y. Pythonfrom sklearn.datasets import load_iris # Load the Iris dataset iris = load_iris() X, y = iris.data, iris.target # Displaying shapes print(f'X shape: {X.shape}') print(f'y shape: {y.shape}') The output will be: textX shape: (150, 4) y shape: (150,) Here, X contains 150 samples, each with 4 features (sepal length, sepal width, petal length, and petal width). The y vector contains 150 class labels, with each label representing one of the three Iris species. This initial step helps us understand the dimensions of our dataset before we proceed with further processing.

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Python

from sklearn.model_selection import train_test_split

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=42)

Python

from sklearn.preprocessing import StandardScaler

# Scale the features
scaler = StandardScaler()
scaler.fit(X_train)
X_train_scaled = scaler.transform(X_train)
X_test_scaled = scaler.transform(X_test)

Python

from sklearn.preprocessing import OneHotEncoder

# One-hot encode the targets
encoder = OneHotEncoder(sparse_output=False)
encoder.fit(y_train.reshape(-1, 1))
y_train_encoded = encoder.transform(y_train.reshape(-1, 1))
y_test_encoded = encoder.transform(y_test.reshape(-1, 1))

Python

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, OneHotEncoder

def load_preprocessed_data():
    # Load the Iris dataset
    iris = load_iris()
    X, y = iris.data, iris.target

    # Split the dataset into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=42)

    # Scale the features
    scaler = StandardScaler().fit(X_train)
    X_train_scaled = scaler.transform(X_train)
    X_test_scaled = scaler.transform(X_test)

    # One-hot encode the targets
    encoder = OneHotEncoder(sparse_output=False).fit(y_train.reshape(-1, 1))
    y_train_encoded = encoder.transform(y_train.reshape(-1, 1))
    y_test_encoded = encoder.transform(y_test.reshape(-1, 1))

    return X_train_scaled, X_test_scaled, y_train_encoded, y_test_encoded

Python

# Load preprocessed data
X_train, X_test, y_train, y_test = load_preprocessed_data()

# Print a sample of one training input and target
print(f'Sample of preprocessed X_train: {X_train[0]}')
print(f'Sample of preprocessed y_train: {y_train[0]}\n')

# Print the shape of scaled and encoded data
print(f'Shape of preprocessed X_train: {X_train.shape}')
print(f'Shape of preprocessed X_test: {X_test.shape}')
print(f'Shape of preprocessed y_train: {y_train.shape}')
print(f'Shape of preprocessed y_test: {y_test.shape}')

text

Sample of preprocessed X_train: [-0.90045861 -1.22024754 -0.4419858  -0.13661044]
Sample of preprocessed y_train: [0. 1. 0.]

Shape of preprocessed X_train: (105, 4)
Shape of preprocessed X_test: (45, 4)
Shape of preprocessed y_train: (105, 3)
Shape of preprocessed y_test: (45, 3)

Introduction

Overview of the Iris Dataset

Insight into Data Preprocessing

Step 1: Loading the Dataset

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Step 2: Splitting into Training and Testing Sets

Step 3: Feature Scaling

Step 4: Target Encoding

Data Preprocessing in Practice

Loading and Printing Preprocessed Data

Lesson Summary and Practice